EdgeModelKit: Sensor Data Acquisition and Logging Library

EdgeModelKit is a Python library developed by EdgeNeuron, designed to simplify sensor data acquisition, logging, and real-time processing for IoT devices. It works seamlessly with the DataLogger script from the EdgeNeuron Arduino library, making it ideal for edge computing and machine learning applications.

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Features

• Serial Communication: Supports data acquisition over serial ports with robust error handling.

• Flexible Data Fetching: Retrieve sensor data as Python lists or NumPy arrays.

• Customizable Logging: Log sensor data into CSV files with optional timestamps and counters.

• Class-Based Organization: Log data with class labels to prepare datasets for machine learning tasks.

• Error Handling: Gracefully handles data decoding errors and missing keys in sensor data packets.

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Usage Prerequisites

This library is designed to work with the DataLogger script available in the EdgeNeuron Arduino library. The DataLogger script configures your Arduino-based IoT device to send structured JSON sensor data over a serial connection.

Before using EdgeModelKit, ensure:

1. Your Arduino device is programmed with the DataLogger script from the EdgeNeuron Arduino library.

2. The device is connected to your system via a serial interface.

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Installation

Install EdgeModelKit using pip:

pip install edgemodelkit  

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Quick Start

1. Initialize the DataFetcher

from edgemodelkit import DataFetcher  

# Initialize the DataFetcher with the desired serial port and baud rate  
fetcher = DataFetcher(serial_port="COM3", baud_rate=9600)  

2. Fetch Sensor Data

# Fetch data as a Python list  
sensor_data = fetcher.fetch_data(return_as_numpy=False)  
print("Sensor Data:", sensor_data)  

# Fetch data as a NumPy array  
sensor_data_numpy = fetcher.fetch_data(return_as_numpy=True)  
print("Sensor Data (NumPy):", sensor_data_numpy)  

3. Log Sensor Data

# Log 10 samples to a CSV file with timestamp and count columns  
fetcher.log_sensor_data(class_label="ClassA", num_samples=10, add_timestamp=True, add_count=True)  

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CSV Logging Details

The CSV file is generated automatically based on the sensor name (e.g., TemperatureSensor_data_log.csv) and contains the following:

• Timestamp: (Optional) Records the time when the data was logged.

• Sample Count: (Optional) A sequential counter for each data sample.

• Data Columns: Each element in the sensor data array is stored in separate columns (e.g., data_value_1, data_value_2, ...).

The data is saved under a folder named Dataset, with subfolders organized by class_label (if specified).

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Real-Time Data Processing Example

from edgemodelkit import DataFetcher  

fetcher = DataFetcher(serial_port="COM3", baud_rate=9600)  

try:  
    while True:  
        # Fetch data as NumPy array  
        sensor_data = fetcher.fetch_data(return_as_numpy=True)  
        print("Received Data:", sensor_data)  

        # Perform custom processing (e.g., feed to a TensorFlow model)  
        # prediction = model.predict(sensor_data)  
        # print("Prediction:", prediction)  
finally:  
    fetcher.close_connection()  

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Dependencies

EdgeModelKit requires the following Python packages:

• numpy

• pandas

• pyserial

• json

Install dependencies with:

pip install numpy pandas pyserial json  

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Contributing

We welcome contributions to EdgeModelKit! Feel free to submit bug reports, feature requests, or pull requests on our GitHub repository.

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License

EdgeModelKit is licensed under the MIT License. See the LICENSE file for details.

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Support

For support and inquiries, contact us at [email protected] or visit our GitHub repository.

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About EdgeNeuron

EdgeNeuron is a pioneer in edge computing solutions, enabling developers to build intelligent IoT applications with state-of-the-art tools and libraries. Learn more at edgeneuronai.com.

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